Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis
Background Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal beca...
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| description | Background Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. Methods and findings A single molecule fluorescence immunosorbent assay was used to detect Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity. Conclusions The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity. |
| doi_str_mv | 10.1371/journal.pone.0275658 |
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Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. Methods and findings A single molecule fluorescence immunosorbent assay was used to detect Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity. Conclusions The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0275658</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Algorithms ; Antibodies ; Background noise ; Biology and Life Sciences ; Biomarkers ; Biosensors ; Calibration ; Care and treatment ; Cell culture ; Computer and Information Sciences ; Coronaviruses ; COVID-19 ; Data mining ; Diagnosis ; Epidemics ; Fluorescence ; Health aspects ; Infectious diseases ; Learning algorithms ; Machine learning ; Medicine and Health Sciences ; Methods ; Modelling ; Optics ; Pathogens ; Patients ; Physical Sciences ; Probabilistic models ; Quantum dots ; Research and Analysis Methods ; Signal to noise ratio ; Silver ; Sustainable development ; Titration ; Tuberculosis</subject><ispartof>PloS one, 2022-10, Vol.17 (10), p.e0275658-e0275658</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Huang et al 2022 Huang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c618t-afbea9138d95090d3bb1f94adee74dd5650af49f03c8ca7bf12ea604bd8b240b3</cites><orcidid>0000-0001-7424-8170 ; 0000-0003-1662-3125</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9595565/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9595565/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids></links><search><contributor>Cowley, Hugh</contributor><creatorcontrib>Huang, Yilun</creatorcontrib><creatorcontrib>Darr, Charles M</creatorcontrib><creatorcontrib>Gangopadhyay, Keshab</creatorcontrib><creatorcontrib>Gangopadhyay, Shubhra</creatorcontrib><creatorcontrib>Bok, Sangho</creatorcontrib><creatorcontrib>Chakraborty, Sounak</creatorcontrib><title>Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis</title><title>PloS one</title><description>Background Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. Methods and findings A single molecule fluorescence immunosorbent assay was used to detect Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity. Conclusions The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.</description><subject>Algorithms</subject><subject>Antibodies</subject><subject>Background noise</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biosensors</subject><subject>Calibration</subject><subject>Care and treatment</subject><subject>Cell culture</subject><subject>Computer and Information Sciences</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Data mining</subject><subject>Diagnosis</subject><subject>Epidemics</subject><subject>Fluorescence</subject><subject>Health aspects</subject><subject>Infectious diseases</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Medicine and Health 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of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis</title><author>Huang, Yilun ; Darr, Charles M ; Gangopadhyay, Keshab ; Gangopadhyay, Shubhra ; Bok, Sangho ; Chakraborty, Sounak</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c618t-afbea9138d95090d3bb1f94adee74dd5650af49f03c8ca7bf12ea604bd8b240b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Antibodies</topic><topic>Background noise</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Biosensors</topic><topic>Calibration</topic><topic>Care and treatment</topic><topic>Cell culture</topic><topic>Computer and Information Sciences</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Data mining</topic><topic>Diagnosis</topic><topic>Epidemics</topic><topic>Fluorescence</topic><topic>Health aspects</topic><topic>Infectious diseases</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Modelling</topic><topic>Optics</topic><topic>Pathogens</topic><topic>Patients</topic><topic>Physical Sciences</topic><topic>Probabilistic models</topic><topic>Quantum dots</topic><topic>Research and Analysis Methods</topic><topic>Signal to noise ratio</topic><topic>Silver</topic><topic>Sustainable development</topic><topic>Titration</topic><topic>Tuberculosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Yilun</creatorcontrib><creatorcontrib>Darr, Charles M</creatorcontrib><creatorcontrib>Gangopadhyay, Keshab</creatorcontrib><creatorcontrib>Gangopadhyay, Shubhra</creatorcontrib><creatorcontrib>Bok, 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tuberculosis</atitle><jtitle>PloS one</jtitle><date>2022-10-25</date><risdate>2022</risdate><volume>17</volume><issue>10</issue><spage>e0275658</spage><epage>e0275658</epage><pages>e0275658-e0275658</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Background Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. Methods and findings A single molecule fluorescence immunosorbent assay was used to detect Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity. Conclusions The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><doi>10.1371/journal.pone.0275658</doi><tpages>e0275658</tpages><orcidid>https://orcid.org/0000-0001-7424-8170</orcidid><orcidid>https://orcid.org/0000-0003-1662-3125</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Antibodies Background noise Biology and Life Sciences Biomarkers Biosensors Calibration Care and treatment Cell culture Computer and Information Sciences Coronaviruses COVID-19 Data mining Diagnosis Epidemics Fluorescence Health aspects Infectious diseases Learning algorithms Machine learning Medicine and Health Sciences Methods Modelling Optics Pathogens Patients Physical Sciences Probabilistic models Quantum dots Research and Analysis Methods Signal to noise ratio Silver Sustainable development Titration Tuberculosis |
| title | Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis |
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